View Source Evision.XFeatures2D.PCTSignatures (Evision v0.2.9)
Summary
Functions
Clears the algorithm state
Computes signature of given image.
Computes signature of given image.
Computes signatures for multiple images in parallel.
Creates PCTSignatures algorithm using sample and seed count. It generates its own sets of sampling points and clusterization seed indexes.
Creates PCTSignatures algorithm using sample and seed count. It generates its own sets of sampling points and clusterization seed indexes.
Variant 1:
Creates PCTSignatures algorithm using pre-generated sampling points and clusterization seeds indexes.
Draws signature in the source image and outputs the result. Signatures are visualized as a circle with radius based on signature weight and color based on signature color. Contrast and entropy are not visualized.
Draws signature in the source image and outputs the result. Signatures are visualized as a circle with radius based on signature weight and color based on signature color. Contrast and entropy are not visualized.
Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read
Generates initial sampling points according to selected point distribution.
This parameter multiplied by the index of iteration gives lower limit for cluster size. Clusters containing fewer points than specified by the limit have their centroid dismissed and points are reassigned.
getDefaultName
Distance function selector used for measuring distance between two points in k-means.
Remove centroids in k-means whose weight is lesser or equal to given threshold.
Color resolution of the greyscale bitmap represented in allocated bits (i.e., value 4 means that 16 shades of grey are used). The greyscale bitmap is used for computing contrast and entropy values.
Number of initial seeds (initial number of clusters) for the k-means algorithm.
Initial seeds (initial number of clusters) for the k-means algorithm.
Number of iterations of the k-means clustering. We use fixed number of iterations, since the modified clustering is pruning clusters (not iteratively refining k clusters).
Threshold euclidean distance between two centroids. If two cluster centers are closer than this distance, one of the centroid is dismissed and points are reassigned.
Maximal number of generated clusters. If the number is exceeded, the clusters are sorted by their weights and the smallest clusters are cropped.
Number of initial samples taken from the image.
Initial samples taken from the image. These sampled features become the input for clustering.
Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)
Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)
Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)
Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)
Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)
Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)
Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)
Size of the texture sampling window used to compute contrast and entropy (center of the window is always in the pixel selected by x,y coordinates of the corresponding feature sample).
Reads algorithm parameters from a file storage
save
This parameter multiplied by the index of iteration gives lower limit for cluster size. Clusters containing fewer points than specified by the limit have their centroid dismissed and points are reassigned.
Distance function selector used for measuring distance between two points in k-means. Available: L0_25, L0_5, L1, L2, L2SQUARED, L5, L_INFINITY.
Remove centroids in k-means whose weight is lesser or equal to given threshold.
Color resolution of the greyscale bitmap represented in allocated bits (i.e., value 4 means that 16 shades of grey are used). The greyscale bitmap is used for computing contrast and entropy values.
Initial seed indexes for the k-means algorithm.
Number of iterations of the k-means clustering. We use fixed number of iterations, since the modified clustering is pruning clusters (not iteratively refining k clusters).
Threshold euclidean distance between two centroids. If two cluster centers are closer than this distance, one of the centroid is dismissed and points are reassigned.
Maximal number of generated clusters. If the number is exceeded, the clusters are sorted by their weights and the smallest clusters are cropped.
Sets sampling points used to sample the input image.
Translations of the individual axes of the feature space.
Translations of the individual axes of the feature space.
Weights (multiplicative constants) that linearly stretch individual axes of the feature space.
Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)
Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)
Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)
Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)
Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)
Weights (multiplicative constants) that linearly stretch individual axes of the feature space.
Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)
Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)
Size of the texture sampling window used to compute contrast and entropy (center of the window is always in the pixel selected by x,y coordinates of the corresponding feature sample).
Stores algorithm parameters in a file storage
write
Types
@type t() :: %Evision.XFeatures2D.PCTSignatures{ref: reference()}
Type that represents an XFeatures2D.PCTSignatures
struct.
ref.
reference()
The underlying erlang resource variable.
Functions
@spec clear(Keyword.t()) :: any() | {:error, String.t()}
@spec clear(t()) :: t() | {:error, String.t()}
Clears the algorithm state
Positional Arguments
- self:
Evision.XFeatures2D.PCTSignatures.t()
Python prototype (for reference only):
clear() -> None
@spec computeSignature(t(), Evision.Mat.maybe_mat_in()) :: Evision.Mat.t() | {:error, String.t()}
Computes signature of given image.
Positional Arguments
self:
Evision.XFeatures2D.PCTSignatures.t()
image:
Evision.Mat
.Input image of CV_8U type.
Return
signature:
Evision.Mat.t()
.Output computed signature.
Python prototype (for reference only):
computeSignature(image[, signature]) -> signature
@spec computeSignature(t(), Evision.Mat.maybe_mat_in(), [{atom(), term()}, ...] | nil) :: Evision.Mat.t() | {:error, String.t()}
Computes signature of given image.
Positional Arguments
self:
Evision.XFeatures2D.PCTSignatures.t()
image:
Evision.Mat
.Input image of CV_8U type.
Return
signature:
Evision.Mat.t()
.Output computed signature.
Python prototype (for reference only):
computeSignature(image[, signature]) -> signature
@spec computeSignatures(t(), [Evision.Mat.maybe_mat_in()], [ Evision.Mat.maybe_mat_in() ]) :: t() | {:error, String.t()}
Computes signatures for multiple images in parallel.
Positional Arguments
self:
Evision.XFeatures2D.PCTSignatures.t()
images:
[Evision.Mat]
.Vector of input images of CV_8U type.
signatures:
[Evision.Mat]
.Vector of computed signatures.
Python prototype (for reference only):
computeSignatures(images, signatures) -> None
Creates PCTSignatures algorithm using sample and seed count. It generates its own sets of sampling points and clusterization seed indexes.
Keyword Arguments
initSampleCount:
integer()
.Number of points used for image sampling.
initSeedCount:
integer()
.Number of initial clusterization seeds. Must be lower or equal to initSampleCount
pointDistribution:
integer()
.Distribution of generated points. Default: UNIFORM. Available: UNIFORM, REGULAR, NORMAL.
Return
- retval:
PCTSignatures
@return Created algorithm.
Python prototype (for reference only):
create([, initSampleCount[, initSeedCount[, pointDistribution]]]) -> retval
@spec create(Keyword.t()) :: any() | {:error, String.t()}
@spec create( [initSampleCount: term(), initSeedCount: term(), pointDistribution: term()] | nil ) :: t() | {:error, String.t()}
Creates PCTSignatures algorithm using sample and seed count. It generates its own sets of sampling points and clusterization seed indexes.
Keyword Arguments
initSampleCount:
integer()
.Number of points used for image sampling.
initSeedCount:
integer()
.Number of initial clusterization seeds. Must be lower or equal to initSampleCount
pointDistribution:
integer()
.Distribution of generated points. Default: UNIFORM. Available: UNIFORM, REGULAR, NORMAL.
Return
- retval:
PCTSignatures
@return Created algorithm.
Python prototype (for reference only):
create([, initSampleCount[, initSeedCount[, pointDistribution]]]) -> retval
@spec create([{number(), number()}], [integer()]) :: t() | {:error, String.t()}
@spec create([{number(), number()}], integer()) :: t() | {:error, String.t()}
Variant 1:
Creates PCTSignatures algorithm using pre-generated sampling points and clusterization seeds indexes.
Positional Arguments
initSamplingPoints:
[Point2f]
.Sampling points used in image sampling.
initClusterSeedIndexes:
[integer()]
.Indexes of initial clusterization seeds. Its size must be lower or equal to initSamplingPoints.size().
Return
- retval:
PCTSignatures
@return Created algorithm.
Python prototype (for reference only):
create(initSamplingPoints, initClusterSeedIndexes) -> retval
Variant 2:
Creates PCTSignatures algorithm using pre-generated sampling points and number of clusterization seeds. It uses the provided sampling points and generates its own clusterization seed indexes.
Positional Arguments
initSamplingPoints:
[Point2f]
.Sampling points used in image sampling.
initSeedCount:
integer()
.Number of initial clusterization seeds. Must be lower or equal to initSamplingPoints.size().
Return
- retval:
PCTSignatures
@return Created algorithm.
Python prototype (for reference only):
create(initSamplingPoints, initSeedCount) -> retval
@spec drawSignature(Evision.Mat.maybe_mat_in(), Evision.Mat.maybe_mat_in()) :: Evision.Mat.t() | {:error, String.t()}
Draws signature in the source image and outputs the result. Signatures are visualized as a circle with radius based on signature weight and color based on signature color. Contrast and entropy are not visualized.
Positional Arguments
source:
Evision.Mat
.Source image.
signature:
Evision.Mat
.Image signature.
Keyword Arguments
radiusToShorterSideRatio:
float
.Determines maximal radius of signature in the output image.
borderThickness:
integer()
.Border thickness of the visualized signature.
Return
result:
Evision.Mat.t()
.Output result.
Python prototype (for reference only):
drawSignature(source, signature[, result[, radiusToShorterSideRatio[, borderThickness]]]) -> result
@spec drawSignature( Evision.Mat.maybe_mat_in(), Evision.Mat.maybe_mat_in(), [borderThickness: term(), radiusToShorterSideRatio: term()] | nil ) :: Evision.Mat.t() | {:error, String.t()}
Draws signature in the source image and outputs the result. Signatures are visualized as a circle with radius based on signature weight and color based on signature color. Contrast and entropy are not visualized.
Positional Arguments
source:
Evision.Mat
.Source image.
signature:
Evision.Mat
.Image signature.
Keyword Arguments
radiusToShorterSideRatio:
float
.Determines maximal radius of signature in the output image.
borderThickness:
integer()
.Border thickness of the visualized signature.
Return
result:
Evision.Mat.t()
.Output result.
Python prototype (for reference only):
drawSignature(source, signature[, result[, radiusToShorterSideRatio[, borderThickness]]]) -> result
@spec empty(Keyword.t()) :: any() | {:error, String.t()}
@spec empty(t()) :: boolean() | {:error, String.t()}
Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read
Positional Arguments
- self:
Evision.XFeatures2D.PCTSignatures.t()
Return
- retval:
bool
Python prototype (for reference only):
empty() -> retval
@spec generateInitPoints([{number(), number()}], integer(), integer()) :: :ok | {:error, String.t()}
Generates initial sampling points according to selected point distribution.
Positional Arguments
initPoints:
[Point2f]
.Output vector where the generated points will be saved.
count:
integer()
.Number of points to generate.
pointDistribution:
integer()
.Point distribution selector. Available: UNIFORM, REGULAR, NORMAL.
Note: Generated coordinates are in range [0..1)
Python prototype (for reference only):
generateInitPoints(initPoints, count, pointDistribution) -> None
@spec getClusterMinSize(Keyword.t()) :: any() | {:error, String.t()}
@spec getClusterMinSize(t()) :: integer() | {:error, String.t()}
This parameter multiplied by the index of iteration gives lower limit for cluster size. Clusters containing fewer points than specified by the limit have their centroid dismissed and points are reassigned.
Positional Arguments
- self:
Evision.XFeatures2D.PCTSignatures.t()
Return
- retval:
integer()
Python prototype (for reference only):
getClusterMinSize() -> retval
@spec getDefaultName(Keyword.t()) :: any() | {:error, String.t()}
@spec getDefaultName(t()) :: binary() | {:error, String.t()}
getDefaultName
Positional Arguments
- self:
Evision.XFeatures2D.PCTSignatures.t()
Return
- retval:
String
Returns the algorithm string identifier. This string is used as top level xml/yml node tag when the object is saved to a file or string.
Python prototype (for reference only):
getDefaultName() -> retval
@spec getDistanceFunction(Keyword.t()) :: any() | {:error, String.t()}
@spec getDistanceFunction(t()) :: integer() | {:error, String.t()}
Distance function selector used for measuring distance between two points in k-means.
Positional Arguments
- self:
Evision.XFeatures2D.PCTSignatures.t()
Return
- retval:
integer()
Python prototype (for reference only):
getDistanceFunction() -> retval
@spec getDropThreshold(Keyword.t()) :: any() | {:error, String.t()}
@spec getDropThreshold(t()) :: number() | {:error, String.t()}
Remove centroids in k-means whose weight is lesser or equal to given threshold.
Positional Arguments
- self:
Evision.XFeatures2D.PCTSignatures.t()
Return
- retval:
float
Python prototype (for reference only):
getDropThreshold() -> retval
@spec getGrayscaleBits(Keyword.t()) :: any() | {:error, String.t()}
@spec getGrayscaleBits(t()) :: integer() | {:error, String.t()}
Color resolution of the greyscale bitmap represented in allocated bits (i.e., value 4 means that 16 shades of grey are used). The greyscale bitmap is used for computing contrast and entropy values.
Positional Arguments
- self:
Evision.XFeatures2D.PCTSignatures.t()
Return
- retval:
integer()
Python prototype (for reference only):
getGrayscaleBits() -> retval
@spec getInitSeedCount(Keyword.t()) :: any() | {:error, String.t()}
@spec getInitSeedCount(t()) :: integer() | {:error, String.t()}
Number of initial seeds (initial number of clusters) for the k-means algorithm.
Positional Arguments
- self:
Evision.XFeatures2D.PCTSignatures.t()
Return
- retval:
integer()
Python prototype (for reference only):
getInitSeedCount() -> retval
@spec getInitSeedIndexes(Keyword.t()) :: any() | {:error, String.t()}
@spec getInitSeedIndexes(t()) :: [integer()] | {:error, String.t()}
Initial seeds (initial number of clusters) for the k-means algorithm.
Positional Arguments
- self:
Evision.XFeatures2D.PCTSignatures.t()
Return
- retval:
[integer()]
Python prototype (for reference only):
getInitSeedIndexes() -> retval
@spec getIterationCount(Keyword.t()) :: any() | {:error, String.t()}
@spec getIterationCount(t()) :: integer() | {:error, String.t()}
Number of iterations of the k-means clustering. We use fixed number of iterations, since the modified clustering is pruning clusters (not iteratively refining k clusters).
Positional Arguments
- self:
Evision.XFeatures2D.PCTSignatures.t()
Return
- retval:
integer()
Python prototype (for reference only):
getIterationCount() -> retval
@spec getJoiningDistance(Keyword.t()) :: any() | {:error, String.t()}
@spec getJoiningDistance(t()) :: number() | {:error, String.t()}
Threshold euclidean distance between two centroids. If two cluster centers are closer than this distance, one of the centroid is dismissed and points are reassigned.
Positional Arguments
- self:
Evision.XFeatures2D.PCTSignatures.t()
Return
- retval:
float
Python prototype (for reference only):
getJoiningDistance() -> retval
@spec getMaxClustersCount(Keyword.t()) :: any() | {:error, String.t()}
@spec getMaxClustersCount(t()) :: integer() | {:error, String.t()}
Maximal number of generated clusters. If the number is exceeded, the clusters are sorted by their weights and the smallest clusters are cropped.
Positional Arguments
- self:
Evision.XFeatures2D.PCTSignatures.t()
Return
- retval:
integer()
Python prototype (for reference only):
getMaxClustersCount() -> retval
@spec getSampleCount(Keyword.t()) :: any() | {:error, String.t()}
@spec getSampleCount(t()) :: integer() | {:error, String.t()}
Number of initial samples taken from the image.
Positional Arguments
- self:
Evision.XFeatures2D.PCTSignatures.t()
Return
- retval:
integer()
Python prototype (for reference only):
getSampleCount() -> retval
@spec getSamplingPoints(Keyword.t()) :: any() | {:error, String.t()}
@spec getSamplingPoints(t()) :: [{number(), number()}] | {:error, String.t()}
Initial samples taken from the image. These sampled features become the input for clustering.
Positional Arguments
- self:
Evision.XFeatures2D.PCTSignatures.t()
Return
- retval:
[Point2f]
Python prototype (for reference only):
getSamplingPoints() -> retval
@spec getWeightA(Keyword.t()) :: any() | {:error, String.t()}
@spec getWeightA(t()) :: number() | {:error, String.t()}
Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)
Positional Arguments
- self:
Evision.XFeatures2D.PCTSignatures.t()
Return
- retval:
float
Python prototype (for reference only):
getWeightA() -> retval
@spec getWeightB(Keyword.t()) :: any() | {:error, String.t()}
@spec getWeightB(t()) :: number() | {:error, String.t()}
Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)
Positional Arguments
- self:
Evision.XFeatures2D.PCTSignatures.t()
Return
- retval:
float
Python prototype (for reference only):
getWeightB() -> retval
@spec getWeightContrast(Keyword.t()) :: any() | {:error, String.t()}
@spec getWeightContrast(t()) :: number() | {:error, String.t()}
Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)
Positional Arguments
- self:
Evision.XFeatures2D.PCTSignatures.t()
Return
- retval:
float
Python prototype (for reference only):
getWeightContrast() -> retval
@spec getWeightEntropy(Keyword.t()) :: any() | {:error, String.t()}
@spec getWeightEntropy(t()) :: number() | {:error, String.t()}
Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)
Positional Arguments
- self:
Evision.XFeatures2D.PCTSignatures.t()
Return
- retval:
float
Python prototype (for reference only):
getWeightEntropy() -> retval
@spec getWeightL(Keyword.t()) :: any() | {:error, String.t()}
@spec getWeightL(t()) :: number() | {:error, String.t()}
Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)
Positional Arguments
- self:
Evision.XFeatures2D.PCTSignatures.t()
Return
- retval:
float
Python prototype (for reference only):
getWeightL() -> retval
@spec getWeightX(Keyword.t()) :: any() | {:error, String.t()}
@spec getWeightX(t()) :: number() | {:error, String.t()}
Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)
Positional Arguments
- self:
Evision.XFeatures2D.PCTSignatures.t()
Return
- retval:
float
Python prototype (for reference only):
getWeightX() -> retval
@spec getWeightY(Keyword.t()) :: any() | {:error, String.t()}
@spec getWeightY(t()) :: number() | {:error, String.t()}
Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)
Positional Arguments
- self:
Evision.XFeatures2D.PCTSignatures.t()
Return
- retval:
float
Python prototype (for reference only):
getWeightY() -> retval
@spec getWindowRadius(Keyword.t()) :: any() | {:error, String.t()}
@spec getWindowRadius(t()) :: integer() | {:error, String.t()}
Size of the texture sampling window used to compute contrast and entropy (center of the window is always in the pixel selected by x,y coordinates of the corresponding feature sample).
Positional Arguments
- self:
Evision.XFeatures2D.PCTSignatures.t()
Return
- retval:
integer()
Python prototype (for reference only):
getWindowRadius() -> retval
@spec read(t(), Evision.FileNode.t()) :: t() | {:error, String.t()}
Reads algorithm parameters from a file storage
Positional Arguments
- self:
Evision.XFeatures2D.PCTSignatures.t()
- func:
Evision.FileNode
Python prototype (for reference only):
read(fn) -> None
save
Positional Arguments
- self:
Evision.XFeatures2D.PCTSignatures.t()
- filename:
String
Saves the algorithm to a file. In order to make this method work, the derived class must implement Algorithm::write(FileStorage& fs).
Python prototype (for reference only):
save(filename) -> None
This parameter multiplied by the index of iteration gives lower limit for cluster size. Clusters containing fewer points than specified by the limit have their centroid dismissed and points are reassigned.
Positional Arguments
- self:
Evision.XFeatures2D.PCTSignatures.t()
- clusterMinSize:
integer()
Python prototype (for reference only):
setClusterMinSize(clusterMinSize) -> None
Distance function selector used for measuring distance between two points in k-means. Available: L0_25, L0_5, L1, L2, L2SQUARED, L5, L_INFINITY.
Positional Arguments
- self:
Evision.XFeatures2D.PCTSignatures.t()
- distanceFunction:
integer()
Python prototype (for reference only):
setDistanceFunction(distanceFunction) -> None
Remove centroids in k-means whose weight is lesser or equal to given threshold.
Positional Arguments
- self:
Evision.XFeatures2D.PCTSignatures.t()
- dropThreshold:
float
Python prototype (for reference only):
setDropThreshold(dropThreshold) -> None
Color resolution of the greyscale bitmap represented in allocated bits (i.e., value 4 means that 16 shades of grey are used). The greyscale bitmap is used for computing contrast and entropy values.
Positional Arguments
- self:
Evision.XFeatures2D.PCTSignatures.t()
- grayscaleBits:
integer()
Python prototype (for reference only):
setGrayscaleBits(grayscaleBits) -> None
Initial seed indexes for the k-means algorithm.
Positional Arguments
- self:
Evision.XFeatures2D.PCTSignatures.t()
- initSeedIndexes:
[integer()]
Python prototype (for reference only):
setInitSeedIndexes(initSeedIndexes) -> None
Number of iterations of the k-means clustering. We use fixed number of iterations, since the modified clustering is pruning clusters (not iteratively refining k clusters).
Positional Arguments
- self:
Evision.XFeatures2D.PCTSignatures.t()
- iterationCount:
integer()
Python prototype (for reference only):
setIterationCount(iterationCount) -> None
Threshold euclidean distance between two centroids. If two cluster centers are closer than this distance, one of the centroid is dismissed and points are reassigned.
Positional Arguments
- self:
Evision.XFeatures2D.PCTSignatures.t()
- joiningDistance:
float
Python prototype (for reference only):
setJoiningDistance(joiningDistance) -> None
Maximal number of generated clusters. If the number is exceeded, the clusters are sorted by their weights and the smallest clusters are cropped.
Positional Arguments
- self:
Evision.XFeatures2D.PCTSignatures.t()
- maxClustersCount:
integer()
Python prototype (for reference only):
setMaxClustersCount(maxClustersCount) -> None
Sets sampling points used to sample the input image.
Positional Arguments
self:
Evision.XFeatures2D.PCTSignatures.t()
samplingPoints:
[Point2f]
.Vector of sampling points in range [0..1)
Note: Number of sampling points must be greater or equal to clusterization seed count.
Python prototype (for reference only):
setSamplingPoints(samplingPoints) -> None
Translations of the individual axes of the feature space.
Positional Arguments
self:
Evision.XFeatures2D.PCTSignatures.t()
idx:
integer()
.ID of the translation
value:
float
.Value of the translation
Note:
WEIGHT_IDX = 0;
X_IDX = 1;
Y_IDX = 2;
L_IDX = 3;
A_IDX = 4;
B_IDX = 5;
CONTRAST_IDX = 6;
ENTROPY_IDX = 7;
Python prototype (for reference only):
setTranslation(idx, value) -> None
Translations of the individual axes of the feature space.
Positional Arguments
self:
Evision.XFeatures2D.PCTSignatures.t()
translations:
[float]
.Values of all translations.
Note:
WEIGHT_IDX = 0;
X_IDX = 1;
Y_IDX = 2;
L_IDX = 3;
A_IDX = 4;
B_IDX = 5;
CONTRAST_IDX = 6;
ENTROPY_IDX = 7;
Python prototype (for reference only):
setTranslations(translations) -> None
Weights (multiplicative constants) that linearly stretch individual axes of the feature space.
Positional Arguments
self:
Evision.XFeatures2D.PCTSignatures.t()
idx:
integer()
.ID of the weight
value:
float
.Value of the weight
Note:
WEIGHT_IDX = 0;
X_IDX = 1;
Y_IDX = 2;
L_IDX = 3;
A_IDX = 4;
B_IDX = 5;
CONTRAST_IDX = 6;
ENTROPY_IDX = 7;
Python prototype (for reference only):
setWeight(idx, value) -> None
Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)
Positional Arguments
- self:
Evision.XFeatures2D.PCTSignatures.t()
- weight:
float
Python prototype (for reference only):
setWeightA(weight) -> None
Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)
Positional Arguments
- self:
Evision.XFeatures2D.PCTSignatures.t()
- weight:
float
Python prototype (for reference only):
setWeightB(weight) -> None
Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)
Positional Arguments
- self:
Evision.XFeatures2D.PCTSignatures.t()
- weight:
float
Python prototype (for reference only):
setWeightContrast(weight) -> None
Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)
Positional Arguments
- self:
Evision.XFeatures2D.PCTSignatures.t()
- weight:
float
Python prototype (for reference only):
setWeightEntropy(weight) -> None
Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)
Positional Arguments
- self:
Evision.XFeatures2D.PCTSignatures.t()
- weight:
float
Python prototype (for reference only):
setWeightL(weight) -> None
Weights (multiplicative constants) that linearly stretch individual axes of the feature space.
Positional Arguments
self:
Evision.XFeatures2D.PCTSignatures.t()
weights:
[float]
.Values of all weights.
Note:
WEIGHT_IDX = 0;
X_IDX = 1;
Y_IDX = 2;
L_IDX = 3;
A_IDX = 4;
B_IDX = 5;
CONTRAST_IDX = 6;
ENTROPY_IDX = 7;
Python prototype (for reference only):
setWeights(weights) -> None
Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)
Positional Arguments
- self:
Evision.XFeatures2D.PCTSignatures.t()
- weight:
float
Python prototype (for reference only):
setWeightX(weight) -> None
Weights (multiplicative constants) that linearly stretch individual axes of the feature space (x,y = position; L,a,b = color in CIE Lab space; c = contrast. e = entropy)
Positional Arguments
- self:
Evision.XFeatures2D.PCTSignatures.t()
- weight:
float
Python prototype (for reference only):
setWeightY(weight) -> None
Size of the texture sampling window used to compute contrast and entropy (center of the window is always in the pixel selected by x,y coordinates of the corresponding feature sample).
Positional Arguments
- self:
Evision.XFeatures2D.PCTSignatures.t()
- radius:
integer()
Python prototype (for reference only):
setWindowRadius(radius) -> None
@spec write(t(), Evision.FileStorage.t()) :: t() | {:error, String.t()}
Stores algorithm parameters in a file storage
Positional Arguments
- self:
Evision.XFeatures2D.PCTSignatures.t()
- fs:
Evision.FileStorage
Python prototype (for reference only):
write(fs) -> None
@spec write(t(), Evision.FileStorage.t(), binary()) :: t() | {:error, String.t()}
write
Positional Arguments
- self:
Evision.XFeatures2D.PCTSignatures.t()
- fs:
Evision.FileStorage
- name:
String
Has overloading in C++
Python prototype (for reference only):
write(fs, name) -> None